A thesis on the application of neural network computing to the constrained flight control allocation problem

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Abstract

The feasibility of utilizing a neural network to solve the constrained flight control
allocation problem is investigated for the purposes of developing guidelines for the
selection of a neural network structure as a function of the control allocation problem
parameters. The control allocation problem of finding the combination of several flight
controls that generate a desired body axis moment without violating any control constraint
is considered. Since the number of controls, which are assumed to be individually linear
and constrained to specified ranges, is in general greater than the number of moments
being controlled, the problem is nontrivial. Parallel investigations in direct and generalized
inverse solutions have yielded a software tool (namely CAT, for Control Allocation
Toolbox) to provide neural network training, testing, and comparison data. A modified
back propagation neural network architecture is utilized to train a neural network to
emulate the direct allocation scheme implemented in CAT, which is optimal in terms of
having the ability to attain all possible moments with respect to a given control surface
configuration. Experimentally verified heuristic arguments are employed to develop
guidelines for the selection of neural network configuration and parameters with respect to
a general control allocation problem. The control allocation problem is shown to be well
suited for a neural network solution. Specifically, a six hidden neuron neural network is
shown to have the ability to train efficiently, fonn an effective neural network
representation of the subset of attainable moments, and independently discover the internal
relationships between moments and controls. The performance of the neural network
control allocator, trained on the basis of the developed guidelines, is examined for the
reallocation of a seven control surface configuration representative of the F/A-18 HARV
in a test maneuver flown using the original control laws of an existing flight simulator.
The trained neural network is found to have good overall generalization performance,
although limitations arise from the ability to obtain the resolution of the direct allocation
scheme at low moment requirements. Lastly, recommendations offered include:
( 1) a proposed application to other unwieldy control al1ocation algorithms, with possible
accounting for control actuator rate limitations, so that the computational superiority of
the neural network could be fully realized; and (2) the exploitation of the adaptive aspects
of neural network computing.